US10438326B2 - Recursive suppression of clutter in video imagery - Google Patents

Recursive suppression of clutter in video imagery Download PDF

Info

Publication number
US10438326B2
US10438326B2 US15/656,808 US201715656808A US10438326B2 US 10438326 B2 US10438326 B2 US 10438326B2 US 201715656808 A US201715656808 A US 201715656808A US 10438326 B2 US10438326 B2 US 10438326B2
Authority
US
United States
Prior art keywords
subimage
subimages
order
image
mth
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active, expires
Application number
US15/656,808
Other languages
English (en)
Other versions
US20190026868A1 (en
Inventor
Dennis J. Yelton
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Boeing Co
Original Assignee
Boeing Co
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Boeing Co filed Critical Boeing Co
Priority to US15/656,808 priority Critical patent/US10438326B2/en
Assigned to THE BOEING COMPANY reassignment THE BOEING COMPANY ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: YELTON, DENNIS J.
Priority to EP18179938.8A priority patent/EP3432258B1/en
Priority to JP2018120681A priority patent/JP7128671B2/ja
Priority to CN201810804758.9A priority patent/CN109286761B/zh
Publication of US20190026868A1 publication Critical patent/US20190026868A1/en
Application granted granted Critical
Publication of US10438326B2 publication Critical patent/US10438326B2/en
Active legal-status Critical Current
Adjusted expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G06T5/002—
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
    • H04N25/60—Noise processing, e.g. detecting, correcting, reducing or removing noise
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/10—Image enhancement or restoration using non-spatial domain filtering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
    • H04N25/60—Noise processing, e.g. detecting, correcting, reducing or removing noise
    • H04N25/67—Noise processing, e.g. detecting, correcting, reducing or removing noise applied to fixed-pattern noise, e.g. non-uniformity of response
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20048—Transform domain processing

Definitions

  • the present disclosure relates generally to imaging and tracking and, in particular, to recursive suppression of clutter in video imagery.
  • Imaging and tracking systems typically include sensors to identify and track objects. For example, some sensors, such as radar systems, send out signals that reflect from objects and are received by the system. Other sensors, such as electro-optical sensors, receive electromagnetic radiation signals from the objects themselves. Improvements in this field have been directed to refining these sensors to be more accurate.
  • electro-optical sensors typically use telescopes and focal plane arrays that detect infrared radiation. Suppression of fixed pattern noise (FPN) is one area of development in electro-optical sensors.
  • FPN fixed pattern noise
  • calibration or non-uniformity correction has been used to suppress fixed pattern noise.
  • this method of fixed pattern suppression may leave a large residual fixed pattern which limits sensor performance and increases sensor noise levels, especially when the raw imagery contains harsh clutter.
  • tracking objects using an optical sensor with a telescope and focal plane array on a moving platform presents additional problems, such as, a need to compensate for the movement of the moving platform.
  • Example implementations of the present disclosure are directed to an improved apparatus, method and computer-readable storage medium for suppressing clutter in video imagery.
  • Example implementations of the present disclosure utilize a recursive motion compensated integration technique to recursively suppress independently moving clutter patterns (or fields) in video imagery, one (but not the only one) of which may be FPN.
  • Example implementations of the present disclosure provide a technical approach for recursively removing clutter patterns in video imagery in a manner that results in superior target detection, especially for dim targets.
  • the suppressed clutter in the video imagery enables the detection of otherwise hard-to-detect targets obscured by even harsh clutter.
  • the present disclosure thus includes, without limitation, the example implementations described below.
  • Some example implementations provide a method of suppressing clutter in video imagery, the method comprising receiving video imagery from a focal plane array; decomposing the video imagery into independently-moving coordinate transformations corresponding to clutter patterns that are subimages of the video imagery; removing the subimages from an image of the video imagery to produce a clutter-suppressed version of the image, including: generating respective zeroth-order corrections for the subimages, and subtracting the respective zeroth-order corrections from the image; and recursively generating respective first-order corrections for the subimages from the respective zeroth-order corrections, and subtracting the respective first-order corrections from the image, wherein the respective zeroth-order corrections are generated and subtracted from the image before the respective first-order corrections are generated and subtracted from the image; and rendering the clutter-suppressed version of the image.
  • decomposing the video imagery includes decomposing the video imagery into the independently-moving coordinate transformations corresponding to clutter patterns, one of which is fixed pattern noise associated with the focal plane array.
  • the image is an image of a kth frame of the video imagery
  • removing the subimages includes removing the subimages including an mth subimage from the image of the kth frame
  • generating the respective zeroth-order corrections comprises generating a zeroth-order correction for the mth subimage of the kth frame, including: removing the DC component from each of N successive images of N successive frames of the video imagery up to and including the kth frame, and thereby producing N successive DC-removed images; transforming the N successive DC-removed images using respective coordinate transformations of the mth subimage from the N successive frames to the kth frame, and thereby producing N transformed images; and accumulating and normalizing the N transformed images to obtain the zeroth-order correction for the mth subimage of the kth frame.
  • the subimages are M subimages
  • the image is an image of a kth frame of the video imagery
  • removing the subimages includes removing the subimages including an mth subimage from the image of the kth frame
  • recursively generating the respective first-order corrections includes recursively generating a first-order correction for the mth subimage of the kth frame from the respective zeroth-order corrections for all M subimages of the kth frame.
  • recursively generating the first-order correction for the mth subimage of the kth frame includes: independently for each subimage excluding the mth subimage: separately transforming the zeroth-order correction for the subimage using a plurality of coordinate transformations each of which is a combination of a coordinate transformation of the subimage from a kth frame of N successive frames of the video imagery to a qth frame of the N successive frames, and a coordinate transformation of the mth subimage from the qth frame to the kth frame, the plurality of coordinate transformations including values of q from 1 to N, separately transforming the zeroth-order correction producing N transformed zeroth-order corrections for the subimage; and accumulating, normalizing and negating the N transformed zeroth-order corrections for the subimage, and thereby producing an accumulated, normalized and negated zeroth-order correction for the subimage; and accumulating the accumulated, normalized and negated zero
  • recursively generating respective first-order corrections includes recursively generating respective first and higher-order corrections for the subimages, and subtracting the respective first-order corrections from the image includes subtracting the respective first and higher-order corrections from the image, and wherein for n ⁇ 0, respective (n+1)st-order corrections are generated from respective nth-order corrections, and the respective nth-order corrections are generated and subtracted from the image before the respective (n+1)st-order corrections are generated and subtracted from the image.
  • the subimages are M subimages
  • the image is an image of a kth frame of the video imagery
  • removing the subimages includes removing the subimages including an mth subimage from the image of the kth frame
  • recursively generating the respective first and higher-order corrections includes recursively generating a (n+1)st-order correction for the mth subimage of the kth frame from the respective nth-order corrections for at least some of the M subimages of the kth frame.
  • recursively generating the (n+1)st-order correction for the mth subimage of the kth frame includes: independently for each subimage of at least some of the M subimages: separately transforming the nth-order correction for the subimage using a plurality of coordinate transformations each of which is a combination of a coordinate transformation of the subimage from a kth frame of N successive frames of the video imagery to a qth frame of the N successive frames, and a coordinate transformation of the mth subimage from the qth frame to the kth frame, the plurality of coordinate transformations including values of q from 1 to N, separately transforming the nth-order correction producing N transformed nth-order corrections for the subimage; and accumulating, normalizing and negating the N transformed nth-order corrections for the subimage, and thereby producing an accumulated, normalized and negated nth-order correction for the subimage; and
  • the at least some of the M subimages are all of the M subimages, and recursively generating the (n+1)st-order correction for the mth subimage of the kth frame further includes subtracting the accumulated, normalized and negated nth-order correction for the mth subimage from the accumulation to obtain the (n+1)st-order correction for the mth subimage of the kth frame.
  • Some example implementations provide an apparatus for suppressing clutter in video imagery, the apparatus comprising a processor configured to cause the apparatus perform a number of operations, including the apparatus being caused to at least perform the method of any preceding example implementation, or any combination thereof.
  • Some example implementations provide a computer-readable storage medium for suppressing clutter in video imagery, the computer-readable storage medium being non-transitory and having computer-readable program code portions stored therein that in response to execution by a processor, cause an apparatus to at least perform the method of any preceding example implementation, or any combination thereof.
  • FIG. 1 schematically illustrates a recursive motion compensation integration (RMCI) system, in accordance with example implementations of the present disclosure
  • FIG. 2 is a functional block diagram of a processor of a computing apparatus configured to cause the computing apparatus to generate a zeroth-order correction for an mth subimage of a kth frame of video imagery, according to some example implementations;
  • FIG. 3 is a functional block diagram of the processor of the computing apparatus configured to cause the computing apparatus to generate an (n+1)st-order correction for the mth subimage of the kth frame, according to some example implementations;
  • FIG. 4 illustrates a flowchart including various operations of a method of suppressing clutter in video imagery, according to some example implementations.
  • FIG. 5 illustrates an apparatus that may correspond to the computing apparatus according to some example implementations.
  • Example implementations of the present disclosure are directed to imaging and tracking and, in particular, to suppression of clutter in video imagery using recursive motion compensation integration (RMCI), and thereby enabling the detection of otherwise hard-to-detect targets obscured by the clutter.
  • RMCI recursive motion compensation integration
  • the imagery acquired by a focal plane array (FPA) is decomposed into independently moving coordinate transformations corresponding to clutter patterns (or fields) that are subimages, each of which is recursively suppressed using detailed information regarding all the subimage motions through the FPA.
  • the FPA contains noticeable fixed pattern noise (FPN), then the FPN forms yet another subimage with zero motion.
  • FPN noticeable fixed pattern noise
  • Example implementations remove each subimage recursively from any desired FPA image by subtracting successive subimage corrections.
  • a zeroth order subimage correction is generated for each clutter pattern (subimage) and subtracted from the desired FPA image.
  • additional corrections are generated recursively and also subtracted from the desired FPA image.
  • first order subimage corrections are calculated from the zeroth order corrections, and subtracted.
  • Second order subimage corrections are then calculated from the first order corrections, and subtracted. And so forth for higher order subimage corrections.
  • the clutter suppression improves with each recursion.
  • the clutter is suppressed a lot with the zeroth order corrections, then substantially more with the first order corrections, then noticeably more with the second order corrections, etc.
  • the successive corrections get smaller and smaller until, eventually, the process reaches a desired amount of removal of the subimages.
  • FIG. 1 schematically illustrates a RMCI system 100 , in accordance with example implementations of the present disclosure.
  • the RMCI system includes an image sensing device such as a focal plane array (FPA) 102 fixedly mounted to a moveable platform 104 .
  • FPA focal plane array
  • a suitable moveable platform include vehicles such as land vehicles (ground vehicles), rail vehicles, aircraft (air vehicles), spacecraft, watercraft and the like.
  • Other examples of a suitable moveable platform include satellites, missiles, advanced kill vehicles and the like.
  • FPA 102 is a component of an optical sensor 106 that also includes a set of optics 108 .
  • the set of optics may be part of a telescope and include one or more lenses, reflectors or the like.
  • the FPA may include a physical array of detectors configured to detect infrared or other wavelengths focused through the set of optics, and generate focal plane array data—or more particularly video imagery—indicative of the same.
  • the detectors of the focal plane array may comprise long band detectors and/or short band detectors, although other types of detectors, such as visible detectors, may be used.
  • the RMCI system 100 includes a computing apparatus 110 in communication with the FPA 102 and generally configured to suppress clutter in video imagery from the FPA to produce a clutter-suppressed version of the video imagery.
  • the computing apparatus is configured to render the clutter-suppressed version of the video imagery for receipt by a target detection processor 112 , presentation by a display 114 and/or storage in non-volatile memory 116 , any one or more of which may be integrated with or separate from the computing apparatus.
  • the computing apparatus includes one or more of each of one or more components such as a processor 118 , one suitable example of which is a field programmable gate array (FPGA).
  • FPGA field programmable gate array
  • the processor 118 is configured to cause the computing apparatus 110 (at times more simply referred to as an “apparatus”) to perform a number of operations.
  • the apparatus is caused to receive video imagery from the FPA 102 , decompose the video imagery into independently-moving coordinate transformations corresponding to clutter patterns that are subimages of the video imagery, and remove the subimages from an image of the video imagery to produce a clutter-suppressed version of the image.
  • one of these independently-moving clutter patterns is fixed pattern noise (FPN) associated with the FPA.
  • FPN fixed pattern noise
  • the apparatus is then caused to render the clutter-suppressed version of the image, such as for receipt by the target detection processor 112 , presentation by the display 114 and/or storage in the non-volatile memory 116 .
  • the processor 118 is configured to cause the computing apparatus 110 to at least generate respective zeroth-order corrections for the subimages, and subtract the respective zeroth-order corrections from the image. Also, the processor is configured to cause the apparatus to recursively generate respective first-order corrections for the subimages from the respective zeroth-order corrections, and subtract the respective first-order corrections from the image. In accordance with example implementations, the respective zeroth-order corrections are generated and subtracted from the image before the respective first-order corrections are generated and subtracted from the image.
  • the video imagery may include any number of images of any number of frames, various example implementations are described below in which the image is an image of an arbitrary kth frame of the video imagery.
  • the subimages may include any number of subimages, various example implementations are described below in which the subimages include an arbitrary nth subimage of perhaps M subimages.
  • the computing apparatus 110 is caused to remove the subimages including the mth subimage from the image of the kth frame.
  • FIG. 2 is a functional block diagram of the processor 118 of the computing apparatus 110 being configured to cause the apparatus to generate a zeroth-order correction for the mth subimage of the kth frame, according to some example implementations. This includes the apparatus being caused to remove the DC component (the arithmetic mean) from each of N successive images of N successive frames of the video imagery up to and including the kth frame, and thereby producing N successive DC-removed images 202 .
  • the DC component the arithmetic mean
  • the computing apparatus 110 is caused to transform 204 the N successive DC-removed images using respective coordinate transformations 206 of the mth subimage from the N successive frames to the kth frame, and thereby produce N transformed images.
  • the respective coordinate transformations may be specified in any of a number of different manners, such as in terms of a mathematical affine transformation or in terms of image optical flow offsets.
  • the respective coordinate transformations may be obtained in any of a number of different manners, such as by a combination of navigational data (e.g., geolocation) and external environment geometry that may be obtained by the moveable platform 104 , or by the direct measurement of subimage motion within the imagery. Regardless of how the respective coordinate transformations are obtained or specified, the N transformed images are then accumulated 208 and normalized 210 to obtain the zeroth-order correction 212 for the mth subimage of the kth frame.
  • the computing apparatus 110 is caused to recursively generate the first-order correction for the mth subimage of the kth frame from the respective zeroth-order corrections for all M subimages of the kth frame. In some more particular examples, this includes, independently for each subimage excluding the mth subimage, the computing apparatus being caused to separately transform the zeroth-order correction for the subimage using a plurality of coordinate transformations to produce transformed zeroth-order corrections for the subimage, and accumulate, normalize and negate the N transformed zeroth-order corrections for the subimage, and thereby produce an accumulated, normalized and negated zeroth-order correction for the subimage. This accumulated, normalized and negated zeroth-order correction is accumulated for the M subimages excluding the mth subimage to obtain the first-order correction for the mth subimage of the kth frame.
  • Each of the plurality of coordinate transformations for the subimage is a combination of a coordinate transformation of the subimage from a kth frame of N successive frames of the video imagery to a qth frame of the N successive frames, and a coordinate transformation of the mth subimage from the qth frame to the kth frame.
  • the plurality of coordinate transforms includes values of q from 1 to N, separately transforming the zeroth-order correction producing N transformed zeroth-order corrections for the subimage. These N transformed zeroth-order corrections for the subimage are then accumulated, normalized and negated to produce the accumulated, normalized and negated zeroth-order correction for the subimage.
  • the apparatus being caused to recursively generate respective first-order corrections includes being caused to recursively generate respective first and higher-order corrections for the subimages.
  • the apparatus is also caused to subtract the respective first-order corrections from the image including being caused to subtract the respective first and higher-order corrections from the image.
  • respective (n+1)st-order corrections are generated from respective nth-order corrections, and the respective nth-order corrections are generated and subtracted from the image before the respective (n+1)st-order corrections are generated and subtracted from the image.
  • the computing apparatus 110 being caused to recursively generate the respective first and higher-order corrections includes being caused to recursively generate a (n+1)st-order correction for the mth subimage of the kth frame from the respective nth-order corrections for at least some of the M subimages of the kth frame.
  • FIG. 3 is a functional block diagram of the processor 118 of the computing apparatus 110 being configured to cause the apparatus to generate an (n+1)st-order correction for the mth subimage of the kth frame, according to some example implementations. As shown, in some examples, this includes, independently for each subimage of at least some of the M subimages, the apparatus being caused to separately transform 302 the nth-order correction 304 for the subimage using a plurality of coordinate transformations 306 .
  • each of these coordinate transformations is a combination of a coordinate transformation of the subimage from a kth frame of N successive frames of the video imagery to a qth frame of the N successive frames, and a coordinate transformation of the mth subimage from the qth frame to the kth frame.
  • the plurality of coordinate transforms include values of q from 1 to N, separately transforming the nth-order correction producing N transformed nth-order corrections for the subimage.
  • the computing apparatus 110 is caused to accumulate 308 , normalize and negate 310 the N transformed nth-order corrections for the subimage, and thereby produce an accumulated, normalized and negated nth-order correction for the subimage.
  • the computing apparatus 110 is caused to accumulate 312 the accumulated, normalized and negated nth-order correction for the at least some of the M subimages to obtain an accumulation that is the (n+1)st-order correction 314 for the mth subimage of the kth frame, or that is from which the (n+1)st-order correction for the mth subimage of the kth frame is obtained.
  • the at least some of the M subimages are all of the M subimages.
  • the apparatus being caused to recursively generate the (n+1)st-order correction further includes being caused to subtract 316 the accumulated, normalized and negated nth-order correction 318 for the mth subimage from the accumulation to obtain the (n+1)st-order correction for the mth subimage of the kth frame.
  • each of the computational chains including 302 - 310 ) feeding into accumulation 312 of the accumulated, normalized and negated nth-order corrections to produce the final accumulation at the right of the figure is entirely independent from the others.
  • each of these computational chains may be simultaneously performed.
  • each subtraction from the accumulation to obtain the (n+1)st-order correction is also independent of the others. So, this could, in principle, also be performed in parallel.
  • FIG. 4 illustrates a flowchart including various operations of a method 400 of suppressing clutter in video imagery, according to some example implementations of the present disclosure.
  • the method includes receiving video imagery from a FPA 102 , and decomposing the video imagery into independently-moving coordinate transformations corresponding to clutter patterns that are subimages of the video imagery.
  • the method also includes removing the subimages from an image of the video imagery to produce a clutter-suppressed version of the image.
  • the removal of the subimages includes generating respective zeroth-order corrections for the subimages, and subtracting the respective zeroth-order corrections from the image, as shown at blocks 408 and 410 .
  • the removal also includes recursively generating respective first-order corrections for the subimages from the respective zeroth-order corrections, and subtracting the respective first-order corrections from the image, as shown at blocks 412 and 414 .
  • the respective zeroth-order corrections are generated and subtracted from the image before the respective first-order corrections are generated and subtracted from the image.
  • the method also includes rendering the clutter-suppressed version of the image, as shown at block 416 .
  • the computing apparatus 110 includes one or more of each of one or more components such as a processor 118 , one suitable example of which is an FPGA.
  • the processor is implemented using FPGAs for most of the image processing because of their extreme speed for many image processing operations.
  • This type of hardware implementation is very appropriate for real-time clutter suppression in high-speed video imagery, but it is not the only possible hardware implementation.
  • the computing apparatus may, in fact, be implemented by various means, including hardware, alone or under direction of one or more computer programs from a computer-readable storage medium.
  • the computing apparatus includes and makes extensive use of graphics processing units (GPUs), which are designed to process many coordinate transformations in parallel.
  • GPUs graphics processing units
  • one or more apparatuses may be provided that are configured to function as, or otherwise implement, the computing apparatus 110 shown and described herein.
  • the respective apparatuses may be connected to, or otherwise be in communication with, one another in a number of different manners, such as directly or indirectly via a wired or wireless network or the like.
  • FIG. 5 more particularly illustrates an apparatus 500 that in some examples may correspond to the computing apparatus 110 .
  • an apparatus of example implementations of the present disclosure may comprise, include or be embodied in one or more fixed or portable electronic devices. Examples of suitable electronic devices include a smartphone, tablet computer, laptop computer, desktop computer, workstation computer, server computer or the like.
  • the apparatus may include one or more of each of a number of components such as, for example, a processor 502 (e.g., processor 118 ) connected to a memory 504 (e.g., storage device).
  • a processor 502 e.g., processor 118
  • memory 504 e.g., storage device
  • the processor 502 is generally any piece of computer hardware that is capable of processing information such as, for example, data, computer programs and/or other suitable electronic information.
  • the processor is composed of a collection of electronic circuits some of which may be packaged as an integrated circuit or multiple interconnected integrated circuits (an integrated circuit at times more commonly referred to as a “chip”).
  • the processor may be configured to execute computer programs, which may be stored onboard the processor or otherwise stored in the memory 504 (of the same or another apparatus).
  • the processor 502 may be a number of processors, a multi-processor core or some other type of processor, depending on the particular implementation. Further, the processor may be implemented using a number of heterogeneous processor systems in which a main processor is present with one or more secondary processors on a single chip. As another illustrative example, the processor may be a symmetric multi-processor system containing multiple processors of the same type. In yet another example, the processor may be embodied as or otherwise include one or more application-specific integrated circuits (ASICs), FPGAs or the like. Thus, although the processor may be capable of executing a computer program to perform one or more functions, the processor of various examples may be capable of performing one or more functions without the aid of a computer program.
  • ASICs application-specific integrated circuits
  • the memory 504 is generally any piece of computer hardware that is capable of storing information such as, for example, data, computer programs (e.g., computer-readable program code 506 ) and/or other suitable information either on a temporary basis and/or a permanent basis.
  • the memory may include volatile and/or non-volatile memory, and may be fixed or removable. Examples of suitable memory include random access memory (RAM), read-only memory (ROM), a hard drive, a flash memory, a thumb drive, a removable computer diskette, an optical disk, a magnetic tape or some combination of the above.
  • Optical disks may include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W), DVD or the like.
  • the memory may be referred to as a computer-readable storage medium.
  • the computer-readable storage medium is a non-transitory device capable of storing information, and is distinguishable from computer-readable transmission media such as electronic transitory signals capable of carrying information from one location to another.
  • Computer-readable medium as described herein may generally refer to a computer-readable storage medium or computer-readable transmission medium.
  • the processor may also be connected to one or more interfaces for displaying, transmitting and/or receiving information.
  • the interfaces may include a communication interface 508 (e.g., communications unit) and/or one or more user interfaces.
  • the communication interface may be configured to transmit and/or receive information, such as to and/or from other apparatus(es), network(s) or the like.
  • the communication interface may be configured to transmit and/or receive information by physical (wired) and/or wireless communications links. Examples of suitable communication interfaces include a network interface controller (NIC), wireless NIC (WNIC) or the like.
  • NIC network interface controller
  • WNIC wireless NIC
  • the user interfaces may include a display 510 (e.g., display 114 ) and/or one or more user input interfaces 512 (e.g., input/output unit).
  • the display may be configured to present or otherwise display information to a user, suitable examples of which include a liquid crystal display (LCD), light-emitting diode display (LED), plasma display panel (PDP) or the like.
  • LCD liquid crystal display
  • LED light-emitting diode display
  • PDP plasma display panel
  • the user input interfaces 512 may be wired or wireless, and may be configured to receive information from a user into the apparatus, such as for processing, storage and/or display. Suitable examples of user input interfaces include a microphone, image or video capture device, keyboard or keypad, joystick, touch-sensitive surface (separate from or integrated into a touchscreen), biometric sensor or the like.
  • the user interfaces may further include one or more interfaces for communicating with peripherals such as printers, scanners or the like.
  • program code instructions may be stored in memory, and executed by a processor, to implement functions of the systems, subsystems and their respective elements described herein.
  • any suitable program code instructions may be loaded onto a computer or other programmable apparatus from a computer-readable storage medium to produce a particular machine, such that the particular machine becomes a means for implementing the functions specified herein.
  • These program code instructions may also be stored in a computer-readable storage medium that can direct a computer, a processor or other programmable apparatus to function in a particular manner to generate a particular machine or particular article of manufacture.
  • the instructions stored in the computer-readable storage medium may produce an article of manufacture, where the article of manufacture becomes a means for implementing functions described herein.
  • the program code instructions may be retrieved from a computer-readable storage medium and loaded into a computer, processor or other programmable apparatus to configure the computer, processor or other programmable apparatus to execute operations to be performed on or by the computer, processor or other programmable apparatus.
  • Retrieval, loading and execution of the program code instructions may be performed sequentially such that one instruction is retrieved, loaded and executed at a time. In some example implementations, retrieval, loading and/or execution may be performed in parallel such that multiple instructions are retrieved, loaded, and/or executed together. Execution of the program code instructions may produce a computer-implemented process such that the instructions executed by the computer, processor or other programmable apparatus provide operations for implementing functions described herein.
  • an apparatus 500 may include a processor 502 and a computer-readable storage medium or memory 504 coupled to the processor, where the processor is configured to execute computer-readable program code 506 stored in the memory. It will also be understood that one or more functions, and combinations of functions, may be implemented by special purpose hardware-based computer systems and/or processors which perform the specified functions, or combinations of special purpose hardware and program code instructions.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)
  • Studio Devices (AREA)
US15/656,808 2017-07-21 2017-07-21 Recursive suppression of clutter in video imagery Active 2038-04-19 US10438326B2 (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
US15/656,808 US10438326B2 (en) 2017-07-21 2017-07-21 Recursive suppression of clutter in video imagery
EP18179938.8A EP3432258B1 (en) 2017-07-21 2018-06-26 Recursive suppression of clutter in video imagery
JP2018120681A JP7128671B2 (ja) 2017-07-21 2018-06-26 ビデオ画像のクラッタの再帰的抑制
CN201810804758.9A CN109286761B (zh) 2017-07-21 2018-07-20 用于抑制视频影像中的杂波的装置和方法

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US15/656,808 US10438326B2 (en) 2017-07-21 2017-07-21 Recursive suppression of clutter in video imagery

Publications (2)

Publication Number Publication Date
US20190026868A1 US20190026868A1 (en) 2019-01-24
US10438326B2 true US10438326B2 (en) 2019-10-08

Family

ID=62909346

Family Applications (1)

Application Number Title Priority Date Filing Date
US15/656,808 Active 2038-04-19 US10438326B2 (en) 2017-07-21 2017-07-21 Recursive suppression of clutter in video imagery

Country Status (4)

Country Link
US (1) US10438326B2 (ja)
EP (1) EP3432258B1 (ja)
JP (1) JP7128671B2 (ja)
CN (1) CN109286761B (ja)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110780272B (zh) * 2019-10-29 2023-06-30 西安电子科技大学 一种颠簸平台sar的非参数化成对回波抑制方法

Citations (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4654665A (en) * 1983-07-21 1987-03-31 Nec Corporation Radar system
US5317395A (en) * 1993-03-31 1994-05-31 The United States Of America As Represented By The Secretary Of The Army Focal plane array dual processing system and technique
US5341142A (en) * 1987-07-24 1994-08-23 Northrop Grumman Corporation Target acquisition and tracking system
US5400161A (en) 1993-10-04 1995-03-21 Raytheon Company Optical system including focus-defocus focal plane array compensation technique using liquid crystal phased array
US5654549A (en) 1994-07-22 1997-08-05 Hughes Electronics Satellite focal plane array imager
US5798786A (en) 1996-05-07 1998-08-25 Recon/Optical, Inc. Electro-optical imaging detector array for a moving vehicle which includes two axis image motion compensation and transfers pixels in row directions and column directions
US5894323A (en) 1996-03-22 1999-04-13 Tasc, Inc, Airborne imaging system using global positioning system (GPS) and inertial measurement unit (IMU) data
US5903659A (en) * 1997-04-17 1999-05-11 Raytheon Company Adaptive non-uniformity compensation algorithm
US6108032A (en) 1996-11-05 2000-08-22 Lockheed Martin Fairchild Systems System and method for image motion compensation of a CCD image sensor
US6211515B1 (en) 1998-10-19 2001-04-03 Raytheon Company Adaptive non-uniformity compensation using feedforward shunting and wavelet filter
US6373522B2 (en) 1996-11-05 2002-04-16 Bae Systems Information And Electronic Systems Integration Inc. Electro-optical reconnaissance system with forward motion compensation
US6714240B1 (en) * 1998-06-23 2004-03-30 Boeing North American, Inc. Optical sensor employing motion compensated integration-device and process
US20050280707A1 (en) 2004-02-19 2005-12-22 Sezai Sablak Image stabilization system and method for a video camera
US20060056724A1 (en) * 2004-07-30 2006-03-16 Le Dinh Chon T Apparatus and method for adaptive 3D noise reduction
US20060251410A1 (en) 2005-05-05 2006-11-09 Trutna William R Jr Imaging device employing optical motion sensor as gyroscope
US20080118104A1 (en) 2006-11-22 2008-05-22 Honeywell International Inc. High fidelity target identification and acquisition through image stabilization and image size regulation
US7907278B1 (en) * 2006-11-06 2011-03-15 Lowell Williams Staring imaging grating spectrometer for detection of projectiles
US20110194734A1 (en) 2005-02-17 2011-08-11 Stmicroelectronics Sa Method for capturing images comprising a measurement of local motions
US20110226955A1 (en) 2005-07-01 2011-09-22 Flir Systems, Inc. Image stabilization system
US20130094694A1 (en) * 2011-10-12 2013-04-18 Raytheon Company Three-frame difference moving target acquisition system and method for target track identification
US8553113B2 (en) 2003-08-20 2013-10-08 At&T Intellectual Property I, L.P. Digital image capturing system and method
US20140015921A1 (en) * 2012-07-16 2014-01-16 Noiseless Imaging Oy Ltd. Methods and systems for suppressing noise in images
US9402028B2 (en) 2012-03-15 2016-07-26 Honeywell International Inc. Image stabilization and tracking system
US9646388B2 (en) 2015-01-09 2017-05-09 The Boeing Company Integrated image distortion correction with motion compensated integration

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2930051B2 (ja) * 1997-04-15 1999-08-03 日本電気株式会社 レーダビデオ信号処理装置
JP3855796B2 (ja) 2002-02-25 2006-12-13 三菱電機株式会社 映像補正装置
JP5889324B2 (ja) * 2011-10-12 2016-03-22 キヤノン株式会社 撮像装置及び撮像装置の制御方法
JP6598660B2 (ja) 2015-12-01 2019-10-30 キヤノン株式会社 画像処理装置および画像処理方法

Patent Citations (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4654665A (en) * 1983-07-21 1987-03-31 Nec Corporation Radar system
US5341142A (en) * 1987-07-24 1994-08-23 Northrop Grumman Corporation Target acquisition and tracking system
US5317395A (en) * 1993-03-31 1994-05-31 The United States Of America As Represented By The Secretary Of The Army Focal plane array dual processing system and technique
US5400161A (en) 1993-10-04 1995-03-21 Raytheon Company Optical system including focus-defocus focal plane array compensation technique using liquid crystal phased array
US5654549A (en) 1994-07-22 1997-08-05 Hughes Electronics Satellite focal plane array imager
US5894323A (en) 1996-03-22 1999-04-13 Tasc, Inc, Airborne imaging system using global positioning system (GPS) and inertial measurement unit (IMU) data
US5798786A (en) 1996-05-07 1998-08-25 Recon/Optical, Inc. Electro-optical imaging detector array for a moving vehicle which includes two axis image motion compensation and transfers pixels in row directions and column directions
US6108032A (en) 1996-11-05 2000-08-22 Lockheed Martin Fairchild Systems System and method for image motion compensation of a CCD image sensor
US6373522B2 (en) 1996-11-05 2002-04-16 Bae Systems Information And Electronic Systems Integration Inc. Electro-optical reconnaissance system with forward motion compensation
US5903659A (en) * 1997-04-17 1999-05-11 Raytheon Company Adaptive non-uniformity compensation algorithm
US6714240B1 (en) * 1998-06-23 2004-03-30 Boeing North American, Inc. Optical sensor employing motion compensated integration-device and process
US6211515B1 (en) 1998-10-19 2001-04-03 Raytheon Company Adaptive non-uniformity compensation using feedforward shunting and wavelet filter
US8553113B2 (en) 2003-08-20 2013-10-08 At&T Intellectual Property I, L.P. Digital image capturing system and method
US20050280707A1 (en) 2004-02-19 2005-12-22 Sezai Sablak Image stabilization system and method for a video camera
US20060056724A1 (en) * 2004-07-30 2006-03-16 Le Dinh Chon T Apparatus and method for adaptive 3D noise reduction
US20110194734A1 (en) 2005-02-17 2011-08-11 Stmicroelectronics Sa Method for capturing images comprising a measurement of local motions
US20060251410A1 (en) 2005-05-05 2006-11-09 Trutna William R Jr Imaging device employing optical motion sensor as gyroscope
US20110226955A1 (en) 2005-07-01 2011-09-22 Flir Systems, Inc. Image stabilization system
US7907278B1 (en) * 2006-11-06 2011-03-15 Lowell Williams Staring imaging grating spectrometer for detection of projectiles
US20080118104A1 (en) 2006-11-22 2008-05-22 Honeywell International Inc. High fidelity target identification and acquisition through image stabilization and image size regulation
US20130094694A1 (en) * 2011-10-12 2013-04-18 Raytheon Company Three-frame difference moving target acquisition system and method for target track identification
US9402028B2 (en) 2012-03-15 2016-07-26 Honeywell International Inc. Image stabilization and tracking system
US20140015921A1 (en) * 2012-07-16 2014-01-16 Noiseless Imaging Oy Ltd. Methods and systems for suppressing noise in images
US9646388B2 (en) 2015-01-09 2017-05-09 The Boeing Company Integrated image distortion correction with motion compensated integration

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Extended European Search Report dated Dec. 5, 2018 in European application No. 18179938.8.
Video Snapshots: Creating high quality images from video clips, Kalyan Sunkavalli et al., IEEE, 1077-2626, 2012, pp. 1868-1879 (Year: 2012). *

Also Published As

Publication number Publication date
EP3432258A1 (en) 2019-01-23
CN109286761A (zh) 2019-01-29
JP7128671B2 (ja) 2022-08-31
CN109286761B (zh) 2021-07-23
US20190026868A1 (en) 2019-01-24
JP2019032824A (ja) 2019-02-28
EP3432258B1 (en) 2020-02-19

Similar Documents

Publication Publication Date Title
US10453187B2 (en) Suppression of background clutter in video imagery
CN109118542B (zh) 激光雷达与相机之间的标定方法、装置、设备及存储介质
US9576375B1 (en) Methods and systems for detecting moving objects in a sequence of image frames produced by sensors with inconsistent gain, offset, and dead pixels
CN105899969A (zh) 飞行时间成像中的快速通用多径校正
US10977808B2 (en) Three-frame difference target acquisition and tracking using overlapping target images
JP6232502B2 (ja) 人工視覚システム
US10495750B1 (en) Spectral replacement to mitigate interference for multi-pass synthetic aperture radar
US20170220896A1 (en) Fault-Aware Matched Filter and Optical Flow
CN112750168A (zh) 事件相机内参的标定方法、装置、计算机设备和存储介质
WO2020209040A1 (ja) 画像処理装置、及び画像処理方法
CN112904359A (zh) 基于远程激光探测与测量的速度估计
CN110706262A (zh) 图像处理方法、装置、设备及存储介质
US20180128621A1 (en) Tracking a target moving between states in an environment
Druml et al. Time-of-flight 3D imaging for mixed-critical systems
US10438326B2 (en) Recursive suppression of clutter in video imagery
KR20200096426A (ko) 동체 검출 장치, 동체 검출 방법, 동체 검출 프로그램
Stepanov et al. Determination of the relative position of space vehicles by detection and tracking of natural visual features with the existing TV-cameras
Zoev et al. Convolutional neural networks of the YOLO class in computer vision systems for mobile robotic complexes
CN110047103B (zh) 追踪图像中目标的位置和取向的方法和系统
US9900509B2 (en) Frame registration for imaging sensors in motion
CN114782484A (zh) 一种针对检测丢失、关联失败的多目标跟踪方法及系统
KR20180097004A (ko) 차량용 레이다 목표 리스트와 비전 영상의 목표물 정합 방법
US9111361B1 (en) Distinguishing between moving targets and clutter in a video
Zhang et al. High-precision and real-time algorithms of multi-object detection, recognition and localization toward ARVD of cooperative spacecrafts
US10535158B2 (en) Point source image blur mitigation

Legal Events

Date Code Title Description
AS Assignment

Owner name: THE BOEING COMPANY, ILLINOIS

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:YELTON, DENNIS J.;REEL/FRAME:043069/0095

Effective date: 20170711

STPP Information on status: patent application and granting procedure in general

Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION

STPP Information on status: patent application and granting procedure in general

Free format text: NOTICE OF ALLOWANCE MAILED -- APPLICATION RECEIVED IN OFFICE OF PUBLICATIONS

STPP Information on status: patent application and granting procedure in general

Free format text: PUBLICATIONS -- ISSUE FEE PAYMENT VERIFIED

STCF Information on status: patent grant

Free format text: PATENTED CASE

MAFP Maintenance fee payment

Free format text: PAYMENT OF MAINTENANCE FEE, 4TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1551); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

Year of fee payment: 4